Machine-to-machine (m2m) sensing applications are becoming increasingly common due to the ubiquity of sensors and the demand for the ability to monitor many variables. Examples of these variables include environment (e.g., temperature, light), infrastructure (e.g., vibrations, strain, breakage), and numerous other applications. Lowering the computing requirements, power consumption, and cost of sensors for m2m sensing may help to increase the use and effectiveness of m2m systems. However, conventional sensors, sensor blocks, and associated components may often continuously operate at full speed, collecting large amounts of data. A sensor block may receive the sensor data and may often transmit the sensor data wirelessly to another device. Further, in some examples, the sensor block may encrypt this data. Accordingly, conventional sensor blocks that receive and convey sensor signals may consume a substantial amount of power.
As an example, suppose an m2m sensor network includes a plurality of sensors and each sensor generates a signal in which the signal bandwidth includes frequencies up to 1 KHz. Thus, an analog-to-digital converter (ADC) may need to perform sampling of the analog signal at a rate of at least 2 KHz, which is the Nyquist rate of the highest frequency in the signal. In addition, a transmitter may transmit data at that same rate, i.e., 2 KHz. Thus, when present, a transmitter may consume a large percentage of the total power consumed by the sensing device. Further, other components of the sensing device may also run at or above the Nyquist rate to match the operating speeds of the ADC and the transmitter.
In addition, a computing device that receives the data from the sensing devices may need to store the large amount of sensor data and may further process the data. For example, the computing device may perform a substantial amount of data analysis and may generate a visual display of the data obtained by the sensors and sensor blocks. Receiving and processing these large amounts of data may lead to high storage costs and may consume a significant amount of processing time and power.
Furthermore, ensuring security of the sensor data sent from the sensor blocks to the computing device and to other sensor block may also drain considerable resources. For instance, sending data over wireless links may employ some mode of encryption, which may consume additional power and resources of the sensor blocks.